from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-25 14:07:41.108335
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Fri, 25, Dec, 2020
Time: 14:07:44
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -44.0542
Nobs: 151.000 HQIC: -45.1220
Log likelihood: 1623.53 FPE: 1.22249e-20
AIC: -45.8526 Det(Omega_mle): 6.86441e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.476734 0.164234 2.903 0.004
L1.Burgenland 0.136435 0.082671 1.650 0.099
L1.Kärnten -0.235370 0.066572 -3.536 0.000
L1.Niederösterreich 0.136730 0.192285 0.711 0.477
L1.Oberösterreich 0.251485 0.164360 1.530 0.126
L1.Salzburg 0.174432 0.085225 2.047 0.041
L1.Steiermark 0.069917 0.118981 0.588 0.557
L1.Tirol 0.154972 0.078649 1.970 0.049
L1.Vorarlberg -0.005751 0.076432 -0.075 0.940
L1.Wien -0.141851 0.159400 -0.890 0.374
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.527093 0.213603 2.468 0.014
L1.Burgenland 0.005531 0.107522 0.051 0.959
L1.Kärnten 0.362109 0.086584 4.182 0.000
L1.Niederösterreich 0.119017 0.250086 0.476 0.634
L1.Oberösterreich -0.179357 0.213766 -0.839 0.401
L1.Salzburg 0.192674 0.110843 1.738 0.082
L1.Steiermark 0.244837 0.154746 1.582 0.114
L1.Tirol 0.146431 0.102290 1.432 0.152
L1.Vorarlberg 0.178296 0.099407 1.794 0.073
L1.Wien -0.579525 0.207316 -2.795 0.005
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.295271 0.071421 4.134 0.000
L1.Burgenland 0.104656 0.035951 2.911 0.004
L1.Kärnten -0.025912 0.028950 -0.895 0.371
L1.Niederösterreich 0.069728 0.083619 0.834 0.404
L1.Oberösterreich 0.291446 0.071475 4.078 0.000
L1.Salzburg -0.003958 0.037062 -0.107 0.915
L1.Steiermark -0.020514 0.051741 -0.396 0.692
L1.Tirol 0.090641 0.034202 2.650 0.008
L1.Vorarlberg 0.128146 0.033238 3.855 0.000
L1.Wien 0.077181 0.069318 1.113 0.266
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.190445 0.082143 2.318 0.020
L1.Burgenland -0.010539 0.041349 -0.255 0.799
L1.Kärnten 0.021859 0.033297 0.657 0.512
L1.Niederösterreich 0.024162 0.096173 0.251 0.802
L1.Oberösterreich 0.411366 0.082206 5.004 0.000
L1.Salzburg 0.096978 0.042626 2.275 0.023
L1.Steiermark 0.190027 0.059509 3.193 0.001
L1.Tirol 0.033038 0.039337 0.840 0.401
L1.Vorarlberg 0.099349 0.038228 2.599 0.009
L1.Wien -0.058262 0.079725 -0.731 0.465
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.571754 0.172357 3.317 0.001
L1.Burgenland 0.071999 0.086760 0.830 0.407
L1.Kärnten 0.008770 0.069865 0.126 0.900
L1.Niederösterreich -0.037413 0.201795 -0.185 0.853
L1.Oberösterreich 0.151630 0.172489 0.879 0.379
L1.Salzburg 0.048964 0.089440 0.547 0.584
L1.Steiermark 0.123689 0.124866 0.991 0.322
L1.Tirol 0.216214 0.082538 2.620 0.009
L1.Vorarlberg 0.012263 0.080212 0.153 0.878
L1.Wien -0.152207 0.167284 -0.910 0.363
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.177849 0.119107 1.493 0.135
L1.Burgenland -0.034665 0.059956 -0.578 0.563
L1.Kärnten -0.016106 0.048280 -0.334 0.739
L1.Niederösterreich 0.169836 0.139450 1.218 0.223
L1.Oberösterreich 0.408922 0.119198 3.431 0.001
L1.Salzburg -0.027278 0.061807 -0.441 0.659
L1.Steiermark -0.050910 0.086288 -0.590 0.555
L1.Tirol 0.192850 0.057038 3.381 0.001
L1.Vorarlberg 0.032844 0.055430 0.593 0.553
L1.Wien 0.156720 0.115601 1.356 0.175
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.220405 0.151014 1.460 0.144
L1.Burgenland 0.071838 0.076017 0.945 0.345
L1.Kärnten -0.043843 0.061213 -0.716 0.474
L1.Niederösterreich -0.026446 0.176807 -0.150 0.881
L1.Oberösterreich -0.117309 0.151129 -0.776 0.438
L1.Salzburg 0.005101 0.078365 0.065 0.948
L1.Steiermark 0.383191 0.109403 3.503 0.000
L1.Tirol 0.524115 0.072318 7.247 0.000
L1.Vorarlberg 0.212488 0.070279 3.023 0.002
L1.Wien -0.227861 0.146569 -1.555 0.120
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.128110 0.175285 0.731 0.465
L1.Burgenland 0.021904 0.088234 0.248 0.804
L1.Kärnten -0.113598 0.071052 -1.599 0.110
L1.Niederösterreich 0.229688 0.205224 1.119 0.263
L1.Oberösterreich 0.008089 0.175419 0.046 0.963
L1.Salzburg 0.221227 0.090960 2.432 0.015
L1.Steiermark 0.128787 0.126987 1.014 0.310
L1.Tirol 0.098363 0.083941 1.172 0.241
L1.Vorarlberg 0.022279 0.081575 0.273 0.785
L1.Wien 0.266666 0.170126 1.567 0.117
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.584758 0.096938 6.032 0.000
L1.Burgenland -0.021924 0.048796 -0.449 0.653
L1.Kärnten 0.001646 0.039294 0.042 0.967
L1.Niederösterreich -0.009311 0.113495 -0.082 0.935
L1.Oberösterreich 0.284097 0.097012 2.928 0.003
L1.Salzburg 0.009634 0.050303 0.192 0.848
L1.Steiermark -0.000188 0.070228 -0.003 0.998
L1.Tirol 0.079342 0.046422 1.709 0.087
L1.Vorarlberg 0.173678 0.045113 3.850 0.000
L1.Wien -0.096782 0.094085 -1.029 0.304
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.136010 -0.012105 0.201356 0.241272 0.043097 0.102997 -0.095015 0.164193
Kärnten 0.136010 1.000000 -0.009884 0.183350 0.132228 -0.155481 0.174148 0.025742 0.298300
Niederösterreich -0.012105 -0.009884 1.000000 0.249268 0.073476 0.188749 0.093963 0.029417 0.344530
Oberösterreich 0.201356 0.183350 0.249268 1.000000 0.271726 0.280299 0.095512 0.066705 0.088452
Salzburg 0.241272 0.132228 0.073476 0.271726 1.000000 0.141712 0.058845 0.074638 -0.035508
Steiermark 0.043097 -0.155481 0.188749 0.280299 0.141712 1.000000 0.098016 0.078104 -0.154625
Tirol 0.102997 0.174148 0.093963 0.095512 0.058845 0.098016 1.000000 0.144570 0.132508
Vorarlberg -0.095015 0.025742 0.029417 0.066705 0.074638 0.078104 0.144570 1.000000 0.093576
Wien 0.164193 0.298300 0.344530 0.088452 -0.035508 -0.154625 0.132508 0.093576 1.000000